Название: Deep Learning-Based Approaches for Sentiment Analysis Автор: Basant Agarwal, Richi Nayak
Издательство: Springer
Серия: Algorithms for Intelligent Systems
Год: 2020
Страниц: 326
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
In recent years, deep Learning approaches have emerged as powerful computational models and have shown significant success to deal with a massive amount of data in unsupervised settings. Deep learning is revolutionizing because it offers an effective way of learning representation and allows the system to learn features automatically from data without the need of explicitly designing them. Deep learning algorithms such as deep autoencoders, convolutional neural network (CNN) and recurrent neural networks (RNN), and long short-term memory (LSTM) have been reported providing significantly improved results in various natural language processing tasks including sentiment analysis.
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